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Running head: NEUROECONOMICS AND CREATIVITY
A Neuroeconomic Framework for Creative Cognition
Hause Lin and Oshin Vartanian
University of Toronto
Corresponding author: Hause Lin Department of Psychology University of Toronto 100 St. George Street, 4th Floor Sidney Smith Hall Toronto, ON M5S 3G3 Canada Email: [email protected]
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Abstract
Neuroeconomics is the study of subjective preferences and value-based decision making. We
present a novel framework that synthesizes findings from the literatures on neuroeconomics and
creativity to provide a neurobiological description of creative cognition. It proposes that
value-based decision-making processes and activity in the locus coeruleus-norepinephrine
(LC-NE) neuromodulatory system underlie creative cognition, as well as the large-scale brain
network dynamics shown to be associated with creativity. This framework allows us to
re-conceptualize creative cognition as driven by value-based decision making, in the process
providing several falsifiable hypotheses that can further our understanding of creativity, decision
making, and brain network dynamics.
Keywords: creativity, neuroeconomics, value-based decision making, locus
coeruleus-norepinephrine (LC-NE) system, network dynamics
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A Neuroeconomic Framework for Creative Cognition
According to the standard definition, products that are both novel and useful within a given
context are considered to be creative (Diedrich, Benedek, Jauk, & Neubauer, 2015; Runco &
Jaeger, 2012; see also Sternberg, 1999). However, despite notable recent advances in the
neuroscience of creativity (for reviews see Jung & Vartanian, in press; Vartanian, Bristol, &
Kaufman, 2013) and a wealth of correlational data from brain imaging studies (for meta-analyses
see Boccia et al., 2015; Gonen-Yaacovi et al., 2013), a critical question that remains unanswered
is how the brain produces ideas that satisfy the above two criteria. Part of this shortcoming may
be due to the lack of mechanistic accounts of brain processes that underlie creative cognition.
We work from the assumption that a complete account of creativity will require not only an
understanding of its cognitive architecture, but also the neural systems that underlie it. Towards
that end, we propose a novel and neurologically plausible framework for creative cognition. This
framework has three components: First, it proposes a neuroeconomic approach to creativity,
suggesting that value-based decision-making processes underlie creative cognition. Second, it
describes how the locus coeruleus-norepinephrine (LC-NE) neuromodulatory system could
support creative cognition by adaptively optimizing utility or subjective value. Third, it suggests
that the dynamic interactions within and between brain networks observed during creative
cognition are driven by activity in the LC-NE system and the interconnected brain regions that
compute and evaluate subjective value. By bringing together a diverse range of findings from
different fields, this framework provides a new conceptualization of creative cognition as driven
by value-based decision making. It also points the way to future research by providing novel and
testable hypotheses that are relevant to the fields of creativity, decision making, and brain
network dynamics.
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NEUROECONOMICS AND CREATIVITY 4
Value-based decision-making processes underlie creative cognition
Neuroeconomics of creative cognition
Neuroeconomics is a young but thriving interdisciplinary field that examines the
neurobiological and computational underpinnings of value-based or economic choices (Camerer,
2013; Rangel, Camerer, & Montague, 2008; Konovalov & Krajbich, 2016). Value-based choices
are pervasive in everyday life, ranging from the mundane to the consequential. Essentially, any
choice that requires us to express our subjective preferences and to choose from among two or
more alternatives is a value-based choice (e.g., Do you want an apple or an orange? Do you
prefer the universe or the multiverse model?). These choices often lack an intrinsically correct
answer, and depend instead on subjective preferences. They are called valued-based or economic
choices because most neurobiological models of decision making have integrated economic
constructs such as utility or value maximization into their frameworks. These models assume that
decision makers make choices by assigning a value to the available options, and then select the
option with the highest value (Kable & Glimcher, 2009; Padoa-Schioppa, 2011; Rangel et al.,
2008).
The basic premise of the present framework is that creative cognition is similarly supported
by value-based decision-making processes. In its stronger form, creative cognition would be
considered a form of value-based decision making. That is, process-wise, creative cognition
resembles making choices in everyday settings because it too involves generating multiple ideas
and then selecting the idea with the highest subjective value amongst generated ideas (see
Vartanian, 2011). Consistent with this proposal, the philosopher Paul Souriau (as cited in
Campbell, 1960, p. 386) noted that “of all of the ideas which present themselves to our mind, we
note only those which have some value and can be utilized in reasoning” (italics added).
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NEUROECONOMICS AND CREATIVITY 5
Moreover, within the psychological literature, the idea that creativity involves thought processes
that resemble value-based decision making is not without precedent. One well-known example is
the family of Blind Variation Selective Retention (BVSR) models, in which creativity includes
generation and selection steps, the latter of which incorporates evaluative processes (Basadur,
Graen, & Green, 1982; Campbell, 1960; Simonton, 1999; see also Vartanian, 2011). Specifically,
after an initial step that involves the generation of candidate ideas, the second step involves the
engagement of evaluative process for selecting the best idea(s) (based on certain criteria).
Another example is Sternberg and Lubart’s investment theory of creativity (Lubart & Sternberg,
1995; Sternberg, 2006, 2012), according to which creative people excel at pursuing and further
developing ideas that have growth potential, but happen to be unknown or out of favor within the
field. In this sense, they “buy low and sell high in the realm of ideas” (Sternberg, 2012, p. 5).
Creative idea generation therefore involves evaluative processes for selecting unpopular ideas for
further nurturing, and is partly influenced by environmental factors that determine the selection
criteria. However, although both BVSR and the investment theory of creativity acknowledge a
relationship between utility maximization and creative cognition, they do not provide a
neurological and mechanistic framework of how utility maximization contributes to creativity. In
what follows we will review evidence suggesting a relationship between value-based decision
making and creativity, and will argue that the former helps to realize the latter.
One of the most robust findings from neuroeconomic research is that across species and
studies, a specific set of brain regions, including the ventromedial prefrontal cortex (vmPFC), the
orbitofrontal cortex (OFC), posterior cingulate cortex (PCC), and the striatum, are involved in
value-based decision making (Padoa-Schioppa & Cai, 2011; Rangel et al., 2008; Rich & Wallis,
2016) (Figure 1). For example, functional magnetic resonance imaging (fMRI) studies have
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shown that blood-oxygen-level dependent (BOLD) signals in the vmPFC correlate with
behavioral preferences for beverages (McClure et al., 2004) and the subjective value of delayed
monetary rewards (Kable & Glimcher, 2007; McClure, Laibson, Loewenstein, & Cohen, 2004).
Crucially, converging evidence from fMRI (Bartra, McGuire, & Kable, 2013; Clithero & Rangel,
2014; Grueschow, Polania, Hare, & Ruff, 2015), lesion (Buckley et al., 2009; Camille, Griffiths,
Vo, Fellows, & Kable, 2011; Hogeveen, Hauner, Chau, Krueger, & Grafman, 2016), and
electrophysiological (Padoa-Schioppa, 2011; Padoa-Schioppa & Assad, 2006; Rich & Wallis,
2016) studies suggests that a set of brain regions comprised of the OFC, vmPFC, medial
prefrontal cortex (mPFC), and PCC not only represent value, but also evaluate choice
alternatives during value-based decision making.
This body of evidence has led to the “common currency” hypothesis, according to which a
small set of specific brain areas appears to encode the subjective values associated with many
different types of rewards on a common neural scale, regardless of the variation in the stimulus
types giving rise to the evaluations (Levy & Glimcher, 2012). Perhaps not surprisingly, the same
set of regions also underlies aesthetic experiences, given that our preferences for attractive faces
(Kim et al., 2007; O'Doherty et al., 2003), harmonious color combinations (Ikeda, Matsuyoshi,
Sawamoto, Fukuyama, & Osaka, 2015), geometrical shapes (Jacobsen, Schubotz, Höfel, &
Cramon, 2006), and paintings or musical excerpts (Ishizu & Zeki, 2011) also reflect the extent to
which we assign subjective value to stimuli of varying reward properties (see also Brown, Gao,
Tisdelle, Eickhoff, & Liotti, 2011; Salimpoor & Zatorre, 2013; Vartanian & Skov, 2014).
Moreover, functional connectivity between the nucleus accumbens and vmPFC predicts how
much participants are willing to spend on musical excerpts (Salimpoor et al., 2013), suggesting
that our evaluative processes can also impact economic choices. These findings suggest that the
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brain networks supporting subjective valuation are also implicated in aesthetic judgements, and
we will argue below that this involvement extends to creative cognition.
Based on findings from neuroeconomics and studies of preference formation, we can
advance a new conceptualization of creativity. Specifically, previous work suggests that two key
processes support creative cognition: generation and evaluation of ideas (Basadur et al., 1982;
Campbell, 1960; Simonton et al., 1999).1 Generation involves coming up with many possible
solutions or ideas in response to a problem (or prompt), whereas evaluation refers to testing those
solutions and ideas and selecting the best option(s) available. Here we posit that these processes
also involve assessing the value or utility of ideas in terms of their novelty and usefulness
(Diedrich et al., 2015; Runco & Jaeger, 2012; Sternberg, 1999). Thus, the current framework
proposes that value-based decision-making processes (e.g., value assignment, representation,
comparison) underlie creative cognition.
Conceptualizing creative cognition as value-based decision-making leads to several novel
hypotheses. First, it predicts that computations in neuroeconomic valuation regions of the brain
(e.g., mPFC, OFC, PCC) should be associated with evaluative processes during creative
cognition. Indeed, this prediction has already found support in fMRI studies that explicitly
compared generative and evaluative processes during creative cognition. For example, Ellamil,
Dobson, Beeman and Christoff (2012) focused on creative drawing, instructing participants in
the fMRI scanner to design book covers and to subsequently evaluate their designs and ideas.
Compared to the generation of drawings, their evaluation was associated with greater activation
in the medial frontal gyrus and PCC, among other regions. In another fMRI study, Mayseless,
Aharon-Peretz, and Shamay-Tsoory (2014) demonstrated that evaluating the originality of ideas
1 Note that we do not make the claim that the generation of ideas is “blind” (see Gabora, 2011).
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NEUROECONOMICS AND CREATIVITY 8
was associated with activation in a set of regions including the PCC. The results of these two
studies are consistent with our predictions, and highlight the role played by neuroeconomic
valuation regions during idea evaluation. Another interesting study in this domain was conducted
by Hao et al. (2016) who demonstrated that in the context of divergent thinking, engagement in
the evaluation of generated ideas compared to a distraction task was associated with higher
originality. In addition, electroencephalogram (EEG) recordings indicated that upper alpha
activity in frontal cortices was greater during idea generation following evaluation, suggesting
that evaluation might “elicit a state of heightened internal attention or top-down activity that
facilitates efficient retrieval and integration of internal memory representations” (p. 30). These
results suggest that creativity benefits from evaluation, and that fMRI and EEG can be used to
examine the localization and dynamics of valuation processes during creative cognition.
Second, because increased fMRI BOLD activity in valuation regions has been associated
with increased subjective value (e.g., Kable & Glimcher, 2007), we also predict that neural
responses in those regions should correlate positively with the quality of ideas (evaluated based
on the attributes of novelty and usefulness) generated during creative cognition. For example,
when performing divergent thinking tasks such as the alternate uses task, participants'
self-reported ratings of originality for their responses should correlate positively with activity in
regions such as the mPFC, OFC, and PCC. Finally, given that neural responses in these valuation
regions can predict economic choices (Smith, Bernheim, Camerer, & Rangel, 2014; Tusche,
Bode, & Haynes, 2010), these neural responses should also predict which idea, out of all the
ideas that have been generated, will be selected eventually by the individual as the best idea.
What makes something creative?
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Neuroeconomics also seeks to develop computational models that specify which decision
variables are computed, how they are computed in distinct brain regions and networks, and how
these computations lead to choices (Rangel & Hare, 2010; Ratcliff, Smith, Brown, & McKoon,
2016; Shadlen & Kiani, 2013; Smith & Ratcliff, 2004). These models have proven fruitful in
various domains such as perceptual decision making (Churchland, Kiani, & Shadlen, 2008; Gold
& Shadlen, 2007), memory (Shadlen & Shohamy, 2016), and self-control (Hare, Camerer, &
Rangel, 2009). We believe that they can also be useful in explaining creative cognition.
The assumption underlying most neurocomputational models is that a noisy relative value
signal accumulates over time, and that decisions are made once the accumulated information or
evidence for one option becomes sufficiently strong to drive choice. For example, a recent study
showed that individuals make altruistic or selfish choices by assigning an overall value to each
option—computed as the weighted sum of two attributes: reward for self and reward for the
other person (Hutcherson, Bushong, & Rangel, 2015). The authors found that the values for these
two attributes were computed independently in distinct brain regions before being integrated and
represented as an overall value signal in the vmPFC. Given that creative ideas are understood to
satisfy the criteria of both novelty and usefulness, in what follows we will outline how
neurocomputational models may provide insights regarding attribute integration necessary for
the emergence of creative ideas.
The assumptions underlying multi-attribute integration computational models of choice
resemble those made in models of aesthetic experiences. Chatterjee and Vartanian (2014, 2016)
suggested that distinct neural systems process different aspects of aesthetic experiences (e.g.,
emotional, perceptual, etc.), and that different weights might be assigned to different systems
that underlie those processes. For example, studies have shown that humans prefer curved over
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sharp objects (Bar & Neta, 2006), and that sharp objects tend to increase activity in the amygdala
(Bar & Neta, 2007), presumably reflecting increased arousal, salience, or sense of threat
associated with sharp objects. Neurocomputational models would thus predict that activity in the
amygdala might reflect one of the many attributes (e.g., sense of threat) that an individual might
take into consideration when computing the overall liking for a sharp or curved object (computed
within the brain’s reward system). Because creative ideas are also defined along multiple
dimensions/attributes (i.e., novelty and usefulness), future work could explore how the values of
different attributes are computed and weighed in distinct brain regions, and how their integration
causes an individual to evaluate the idea or product high or low on creativity. This proposal is
consistent with Martindale’s (1984) theory of cognitive hedonics, according to which thoughts
(e.g., ideas) have evaluative aspects, which in turn can drive one’s preference for and continued
pursuit of certain ideas over others. If the common currency hypothesis is correct (e.g., Levy &
Glimcher, 2012), then the evaluation of ideas should also occur within the same neural network
that computes subjective values for all other stimulus types.
Locus coeruleus-norepinephrine (LC-NE) system supports creative cognition
Exploration and exploitation of ideas
When an idea has high utility (e.g., it is novel and useful), it is often advantageous to
exploit the utility the idea provides. In contrast, if an idea has low utility (e.g., it lacks novelty
and/or usefulness), it may be preferable to explore other ideas to find better alternatives. Many
decisions in our daily lives require us to make a trade-off between exploitation and exploration
(Christian & Griffiths, 2016; Cohen, McClure, & Yu, 2007). Do we try new things or stick with
existing ones? Which pizza to order? Should you get your “usual” or ask about the specials?
Which ideas should one pursue? For example, when inventing a new product, should you
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continue developing new ideas after having generated n number ideas or should you start to
focus on and develop one of them further? The current framework proposes that creative
cognition is mediated by processes that resemble those apparent in classic
exploitation-exploration dilemmas. In this section, we will outline how activity in the locus
coeruleus-norepinephrine (LC-NE) neuromodulatory system might support creative cognition by
mediating the transitions between idea exploration and exploitation.
When people are initially trying to find inspiration or ideas for tackling a new problem,
they are often attempting to explore and generate ideas that satisfy certain criteria. These criteria
may be based on abstract top-down goals that determine how much utility or value is assigned to
any particular idea. For example, an artist might be seeking an idea that best conveys a particular
meaning or emotion, and a scientist might be developing a new experimental procedure that most
stringently tests a theoretical prediction. That is, these individuals are generating ideas by
exploring the available options and pruning them by assessing their utilities. Different ideas will
have different utilities depending on how well they satisfy certain criteria. Most ideas will likely
be entertained very briefly because they fail to satisfy those criteria, and are subsequently
discarded. However, when individuals land on an idea that satisfies those criteria sufficiently,
they will likely stop exploring additional ideas because they would want to devote their time and
resources to fully exploit the utility it provides. The present framework suggests that the creative
process described above reflects an adaptive utility-optimization process that is mediated by
activity in the LC-NE system and interconnected brain regions that compute and evaluate
subjective value.
Locus coeruleus-norepinephrine system and function
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The locus coeruleus nucleus sits deep in the pons and sends noradrenergic projections to
nearly all brain regions (with the notable exception of the basal ganglia and hypothalamus), and
is the only source of norepinephrine to the cerebral, cerebellar, and hippocampal cortices (Foote
& Morrison, 1987; Moore & Bloom, 1979) (Figure 2). Because of locus coeruleus' diffuse
projections to cortical regions, early research focused primarily on its role in cognitive processes
(Amaral & Sinnamon, 1977), especially in mediating arousal (Berridge & Waterhouse, 2003).
The LC-NE system's role in regulating cognitive processing and arousal was motivated by the
observation that salient and arousing stimuli reliably elicit a phasic activation of locus coeruleus
neurons and norepinephrine release in cortical target sites (Aston-Jones & Bloom, 1981; Brun,
Suaud-Chagny, Gonon, & Buda, 1993; Hervé-Minvielle & Sara, 1995). Phasic activation is
defined as a rapid response of short duration (in contrast to tonic activation that evolves more
slowly but is of longer duration). Moreover, norepinephrine has also been found to modulate the
arousal and gain (i.e., responsiveness) of signals in cortical regions (Devilbiss & Waterhouse,
2000). More recent work suggests that the LC-NE system is also implicated in decision making
and utility optimization (Aston-Jones & Cohen, 2005a-b; Aston-Jones & Waterhouse, 2016),
which have particularly relevant roles and functions in the present conceptualization of creative
cognition.
Early work on creativity and the LC-NE system has already suggested a relationship
between noradrenergic activity, arousal, and creativity. For example, early theories of LC-NE
function emphasized the system's role in modulating arousal and cognitive processing (e.g.,
Berridge & Waterhouse, 2003), and early work on creativity demonstrated that low levels of
arousal were associated with increased creativity (Martindale & Greenough, 1973). During
creative generation, more creative individuals show stronger electroencephalogram (EEG) alpha
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NEUROECONOMICS AND CREATIVITY 13
band activity (Martindale & Hasenfus, 1978), which is believed to reflect reduced arousal
mediated by noradrenergic projections from the locus coeruleus (Foote, Berridge, Adams, &
Pineda, 1991; Foote & Morrison, 1987). Moreover, noradrenergic activity has been associated
with cognitive flexibility (Heilman, Nadeau, & Beversdorf, 2003; Heilman, 2016; see also
Beversdorf, 2013), which is typically measured by tasks that require one to search through a
network to identify a solution (e.g., anagrams, compound remote associates task; see Bowden &
Jung-Beeman, 2003). For example, studies in rats and humans have shown that reducing
noradrenergic activity by administering propranolol (a beta-adrenergic antagonist) improved
performance on cognitive flexibility tasks (Alexander, Hillier, Smith, Tivarus, & Beversdorf,
2007; Campbell, Tivarus, Hillier, & Beversdorf, 2008; Hecht, Will, Schachtman, Welby, &
Beversdorf, 2014). Although when taken together these findings suggest a link between
noradrenergic activity, arousal, and creativity, they have not been integrated into a unified
framework.
Phasic vs. tonic locus coeruleus activity drive idea exploration vs. exploitation
The adaptive gain theory of LC-NE function (Aston-Jones & Cohen, 2005b) may provide
the necessary insights into the neural processes underlying creative cognition by linking
neuroeconomic findings with work relating noradrenergic activity to creativity. According to the
adaptive gain theory, LC-NE activity falls on a continuum from phasic to tonic. The present
framework suggests that phasic locus coeruleus activity is associated with creative processes that
exploit or evaluate high-utility ideas (e.g., creative evaluation), whereas tonic locus coeruleus
activity is associated with processes that involve exploring many potential ideas (e.g., creative
generation). In a phasic mode, strong bursts of locus coeruleus activity are tightly coupled with
task-related decision processes and indicate that the current task has high utility. Phasic locus
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NEUROECONOMICS AND CREATIVITY 14
coeruleus activity improves performance and promotes exploitation by filtering irrelevant
responses by increasing gain (i.e., responsiveness) of processing and reducing noise in cortical
regions (Aston-Jones & Cohen, 2005b; Mather, Clewett, Sakai, & Harley, 2016). In the context
of creative cognition, phasic locus coeruleus activity should reflect and promote the exploitation
of a high utility idea. In a tonic mode, phasic locus coeruleus activity is reduced or absent but
tonic activity is increased; this mode is associated with reduced neural gain, low current task
utility, task disengagement, and increased distractibility. During creative cognition, the lack of
high utility ideas (e.g., during creative generation) may be associated with the lack of focus on
any particular idea and increased tonic locus coeruleus activity, which, in turn, increases baseline
norepinephrine release that increases noise in the system to encourage exploration of alternative
solutions or ideas (Usher, Cohen, Servan-Schreiber, Rajkowski, & Aston-Jones, 1999).
Critically, the adaptive gain theory suggests that whether LC-NE activity is in phasic or
tonic mode depends on value computations in cortical regions such as the OFC (Padoa-Schioppa
& Assad, 2006) and the anterior cingulate cortex (ACC; Heilbronner & Hayden, 2016; Shenhav
et al., 2013)— both of which project densely to the locus coeruleus (Aston-Jones & Cohen,
2005a; Porrino & Goldman-Rakic, 1982). Furthermore, during creative cognition,
neuroeconomic valuation regions (e.g., OFC) are hypothesized to drive and produce the
transitions between phasic and tonic activity in the locus coeruleus. When a newly generated
idea is novel and useful, the valuation regions would assign a high utility to this idea, leading to
locus coeruleus phasic activity that promotes exploitation of that idea. But when creative ideas
are absent, the valuation regions would register low overall utility, sending signals to the locus
coeruleus to initiate a shift towards tonic locus coeruleus mode, which would in turn increase
baseline norepinephrine release and facilitate exploring and sampling of other ideas that might
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NEUROECONOMICS AND CREATIVITY 15
provide higher utility. In sum, the subjective value assigned to ideas (determined by how well
these ideas satisfy certain criteria) is hypothesized to drive phasic and tonic locus coeruleus
activity, which in turn serves to maximize long-term utility by optimizing the trade-off between
idea exploitation and exploration.
Reinterpretation of existing findings and new predictions
By extending the adaptive gain theory of LC-NE function to creative cognition, the present
framework not only helps to reinterpret and integrate existing findings but also makes new
predictions. First, because creative generation is more strongly associated with idea exploration
whereas creative evaluation is more strongly associated with idea exploitation, it follows that
generation and evaluation should be associated with tonic and phasic locus coeruleus activity,
respectively. This prediction can be tested by measuring fMRI BOLD activity in the locus
coeruleus (see Murphy, O'Connell, O'Sullivan, Robertson, & Balsters, 2014) during creative
generation and evaluation. The P3 event-related potential and pupil diameter have also been
shown to correlate with LC-NE activity (Murphy, Robertson, Balsters, & O'Connell, 2011;
Nieuwenhuis, Aston-Jones, & Cohen, 2005), with baseline pupil diameter correlating remarkably
well with locus coeruleus firing rates, such that larger pupil diameters reflect increased tonic
locus coeruleus activity (Aston-Jones & Cohen, 2005b; Rajkowski, Kubiak, & Aston-Jones,
1994). Moreover, exploratory choices during a reinforcement learning gambling task were
associated with larger pupil diameter, and changes in pupil size correlated with changes in task
utility and the transition between exploitation and exploration (Jepma & Nieuwenhuis, 2011).
Given that the present framework proposes that utility computation, exploitation, and exploration
processes underlie creative cognition, pupil diameter may provide a useful tool for studying
creative processes. These findings further highlight the utility of the present framework because
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NEUROECONOMICS AND CREATIVITY 16
it proposes several measures (e.g., pupil diameter, locus coeruleus BOLD activity, P3; see also
Mather et al., 2017) that can be used to study the processes underlying creative cognition.
Second, since creative people are usually better at generating more and better ideas, the
current framework would predict that creative people should have increased levels of tonic or
baseline locus coeruleus activity and norepinephrine, which are the neurobiological processes
that facilitate idea exploration and generation. Although there is no direct evidence for these
individual differences in baseline locus coeruleus activity in relation to creativity, the locus
coeruleus has been associated with cognitive function and abilities (Mather & Harley, 2016).
Indeed, a recent study found that baseline pupil size (a proxy for locus coeruleus activity)
correlates with intelligence (Tsukahara, Harrison, & Engle, 2016), which is a factor that predicts
individual differences in creativity (Benedek, Jauk, Sommer, Arendasy, & Neubauer, 2014; Jauk,
Benedek, Dunst, & Neubauer, 2013; Jauk, Benedek, & Neubauer, 2014; Nusbaum & Silvia,
2011). Thus, the present proposal has the potential to provide an integrative framework that
explains not only the bases of creative processes, but also individual differences in creativity.
Third, increased tonic locus coeruleus activity and norepinephrine should predispose
creative people to increased distractibility because norepinephrine reduces neural gain and
increases noise in the system. These changes suggest that creative people may be more likely to
experience sensory overstimulation because of their over-inclusive attention. Consistent with
these predictions, many studies have reported that creative people tend to be oversensitive (e.g.,
Martindale, Anderson, Moore, & West, 1996; Martindale & Armstrong, 1974). Moreover, a
recent study found that real-world creative achievement was associated with 'leaky attention'
(Zabelina, O'Leary, Pornpattananangkul, Nusslock, & Beeman, 2015), which was reflected in
reduced sensory gating as indexed by the P50 event-related potential. These findings suggest that
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NEUROECONOMICS AND CREATIVITY 17
real-world creative people might be less able to filter irrelevant information—a filtering process
mediated by phasic rather than tonic locus coeruleus activity—and that such leaky sensory gating
(mediated by increased tonic locus coeruleus activity) might be one of the processes that benefit
creativity by focusing attention on more stimuli regardless of their relevance (Mendelsohn &
Griswold, 1964; Russell, 1976). In addition, creative people are often able to connect distantly
related concepts or ideas, presumably because increased tonic locus coeruleus activity increases
noise and leaky sensory gating, allowing them to make use of more stimuli and cues (Ansburg &
Hill, 2003).
Further support for the relationship between tonic locus coeruleus activity and leaky
sensory gating comes from recent work showing that pupil diameter reflects locus
coeruleus-driven neural gain and sensory processing, such that higher gain (i.e., phasic locus
coeruleus activity) was associated with narrow attentional focus whereas lower gain (i.e., tonic
locus coeruleus activity) was associated with broader attentional focus (Eldar, Cohen, & Niv,
2013; Eldar, Niv, & Cohen, 2016). Despite the evidence linking increased tonic locus coeruleus
activity with creativity, it may also be that creative people are better at switching between the
two modes of locus coeruleus activity because creative cognition involves generating and
evaluating ideas, believed to be mediated by tonic and phasic locus coeruleus activity
respectively. Indeed, the mechanisms that determine switching between different modes of
cognition in the service of creative problem solving remain one of the major open questions in
the area (see Dorfman, Martindale, Gassimova, & Vartanian, 2008; Vartanian, 2009; Vartanian,
Martindale, & Kwiatkowski, 2007).
Fourth, the present framework ascribes a central role for the LC-NE system, leading to the
prediction that disturbances in the LC-NE system should affect creative cognition. Consistent
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NEUROECONOMICS AND CREATIVITY 18
with this prediction, the LC-NE system has been implicated in highly overlapping sets of clinical
disorders (e.g., schizophrenia, depression, attention deficit disorder, bipolar disorder) associated
with either enhanced or impaired creativity (Baas, Nijstad, Boot, & De Dreu, 2016; Kyaga et al.,
2011; Simonton, 2014). That is, disturbances in the LC-NE system may affect processes such as
sensory gating, utility optimization, and decision making, which, in turn, may be risk factors for
clinical disorders and may thus provide an explanatory factor in the link between creativity and
clinical disorders.
Valuation processes and LC-NE activity mediate creative cognition network dynamics
Recent neuroimaging work has converged on the view that creative cognition involves
dynamic interactions within and between large-scale brain networks, especially the default mode
network (DMN) and the executive control network (Beaty, Benedek, Kaufman, & Silvia, 2015;
Ellamil et al., 2012; Liu et al., 2015). These findings have led to the idea that the DMN might
support creative idea generation, whereas the executive control network modulates activity in the
DMN to ensure that task goals are met (Beaty, Benedek, Silvia, & Schacter, 2016). Although
these brain networks are clearly implicated in creative cognition, one critical question remains
unaddressed: What determines the engagement of these networks, as well as the interactions and
transitions between them? The present framework speculates that network dynamics observed
during creative cognition are driven by value computations in regions within the brain’s
valuation system (Figure 1) and activity in the LC-NE system (Figure 2), which optimizes the
trade-off between idea exploitation and exploration.
The core brain regions that assign, represent, and evaluate subjective value during
value-based decision making are the OFC, vmPFC, and PCC (Bartra et al., 2013; Clithero &
Rangel, 2014). Coincidentally, the mPFC and PCC also form the core of the DMN
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NEUROECONOMICS AND CREATIVITY 19
(Andrews-Hanna, Reidler, Sepulcre, Poulin, & Buckner, 2010; Zabelina & Andrews-Hanna,
2016), which has been implicated in creative cognition (Beaty et al., 2015, 2016), spontaneous
thought (Christoff, Irving, Fox, Spreng, & Andrews-Hanna, 2016; Mittner, Hawkins, Boekel, &
Forstmann, 2016; Smallwood & Schooler, 2015), and internally-oriented cognition such as
self-generated thought (Andrews-Hanna, Smallwood, & Spreng, 2014; Zabelina &
Andrews-Hanna, 2016). These anatomical and functional overlaps suggest that DMN activity
might reflect value-based decision-making processes proposed to underlie creative cognition in
the present framework.
Multiple lines of work also suggest that the PCC might play a crucial role during creative
cognition. Apart from being implicated in value-based decision making (Bartra et al., 2013;
Grueschow et al., 2015) and internally-oriented and creative cognition (Zabelina &
Andrews-Hanna, 2016; Christoff et al., 2016), the PCC might mediate functional coupling and
transitions between different brain networks. For example, during early phases of divergent
thinking, the PCC was strongly coupled with the salience network regions (e.g., insula), whereas
during later phases it was coupled with executive control network regions (e.g., dorsal lateral
PFC; Beaty et al., 2015). These findings suggest that computations in the PCC might be critical
for engaging different brain networks, as well as mediating network interactions and transitions.
Another related possibility is that DMN activity is more closely associated with an
exploratory mode of cognition, whereas default suppression is more closely associated with an
exploitative mode. In turn, the PCC might mediate shifts between these two modes (Pearson,
Hayden, Raghavachari, & Platt, 2009; Pearson, Heilbronner, Barack, Hayden, & Platt, 2011).
For example, one study reported increased activity in the PCC during the period leading up to an
insightful solution (Kounios et al., 2006). PCC activity during this period might reflect processes
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NEUROECONOMICS AND CREATIVITY 20
than mediate the shift from exploration (i.e., finding potential solutions) to exploitation (i.e., an
insightful solution has been found). Given that the PCC is involved in detecting changes in the
environment and mediating subsequent changes in behavior (Pearson et al., 2011), it may be that
the PCC plays an important role in detecting changes in the overall utility of ideas during
creative cognition, and mediates the shift between different brain networks.
In addition to the DMN and executive control network, the salience network might also
play an important role during creative cognition by initiating transitions between brain networks.
For example, the PCC shows increased functional coupling with the insular and ACC during
initial phases of divergent thinking (Beaty et al., 2015). Critically, the insular and ACC form the
core of the salience network, which is believed to play a central role in dynamically coordinating
attention and initiating the switch between the default and executive control networks (Cocchi,
Zalesky, Fornito, & Mattingley, 2013; Uddin, 2015). During creative cognition, it may be that
the salience network facilitates transitions between idea exploration (generation) and exploitation
(evaluation).
Importantly, the insular cortex, ACC, OFC, and locus coeruleus nucleus are highly
interconnected, suggesting that transitions between LC-NE phasic and tonic activity could be
associated with activity in the salience network (ACC and insular cortex). The OFC and ACC
send major cortical inputs to the locus coeruleus (Aston-Jones & Cohen, 2005a; Porrino &
Goldman-Rakic, 1982); the OFC projects to the insular cortex (Aston-Jones & Cohen, 2005a),
which also projects to the OFC and ACC (Aston-Jones & Cohen, 2005a-b). These
neuroanatomical interconnections raise the possibility that value computations drive LC-NE
activity, which, in turn, mediates interactions and transitions between various brain networks.
That is, LC-NE activity might play a central role in governing network dynamics, and this idea is
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NEUROECONOMICS AND CREATIVITY 21
consistent with the view that the fMRI BOLD signal may reflect neuromodulatory effects more
than changes in the spiking rate of neurons (Logothetis, 2008).
The idea that LC-NE activity might drive network dynamics is also consistent with other
models of LC-NE function. LC-NE activity has been proposed to facilitate network resetting,
such that when the LC-NE system is activated, it resets the system by interrupting existing
functional networks and facilitating the emergence of new ones (Bouret & Sara, 2005; Sara,
2009; see also Mittner et al., 2016). For example, it may be that norepinephrine released by the
locus coeruleus would reset the attention networks to promote adaptive shifts in attention and
changes in behavioral responses (Corbetta, Patel, & Shulman, 2008; Sara & Bouret, 2012).
During creative cognition, such attention resetting might facilitate the transition from idea
exploration to exploitation. Integrating these theories of LC-NE function is beyond the scope of
the current paper, but with the present framework we hope to stimulate future theoretical and
empirical work that bridges LC-NE function, creative cognition, and value-based decision
making.
Limitations and future directions
By synthesizing ideas and findings from multiple fields, the present framework offers a
novel account of creative cognition. However, several issues remain to be addressed. First, this
framework assumes that creative cognition is not qualitatively different from normal cognition,
in that decision processes that underlie everyday choices are assumed to also support creative
processes. However, creative and normal cognition could be seen as mutually exclusive, partially
overlapping, or undifferentiated (Abraham, 2013). Clearly, the present framework is
incompatible with the mutual exclusivity account, but future work should explore whether the
processes underlying creative cognition and economic choice are partially or completely
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NEUROECONOMICS AND CREATIVITY 22
overlapping. Second, in its current conceptualization, this framework does not distinguish
between the various aspects or types of creativity such as divergent thinking, insight problem
solving, combination of remote semantic associations, etc. It assumes that the same value-based
decision-making processes underlie creative cognition during all of the above, but future work is
required to test this assumption. Third, the current framework has the potential to provide an
integrative framework that explains not only creative processes within an individual, but also
individual differences in creativity. However, more work is needed to test this aspect of the
model. Fourth, this paper has discussed creative generation and evaluation as though these two
processes occur independently. However, like LC-NE phasic and tonic activity which falls on a
continuum, generative and evaluative processes might also fall on a continuum, or it could be
that the transitions between these processes might occur too rapidly to be measured using tools
that have relatively low temporal resolution (e.g., fMRI). Thus, other neuroimaging methods
with greater temporal resolution might be better suited to test some of the framework's
predictions.
Conclusion
Recently, several frameworks have been proposed to account for the neural mechanisms
that underlie creativity (Boot, Baas, van Gaal, Cools, & De Dreu, 2017; Dietrich & Haider,
2016). Unlike previous accounts, the framework proposed here draws heavily on
neuroeconomics to provide an account of how creative cognition occurs in the brain. By thinking
about creative cognition as an adaptive value-maximization process supported by activity in the
LC-NE neuromodulatory system, it re-conceptualizes the way we think about the creative
process and provides a novel perspective that can be used to reinterpret existing findings.
Crucially, it offers many new hypotheses, making it testable and falsifiable. Although here we
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NEUROECONOMICS AND CREATIVITY 23
have outlined some key predictions based on our model, many additional nuanced predictions
can be derived from it. Importantly, this framework has the potential to improve our
understanding of not just creative cognition, but also the relationship between decision making,
neuromodulation, and large-scale brain network dynamics.
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NEUROECONOMICS AND CREATIVITY 24
References
Abraham, A. (2013). The promises and perils of the neuroscience of creativity. Frontiers in
Human Neuroscience, 7, 246. doi:10.3389/fnhum.2013.00246
Alexander, J. K., Hillier, A., Smith, R. M., Tivarus, M. E., & Beversdorf, D. Q. (2007).
Beta-adrenergic modulation of cognitive flexibility during stress. Journal of Cognitive
Neuroscience, 19, 468-478. doi:10.1162/jocn.2007.19.3.468
Amaral, D. G., & Sinnamon, H. M. (1977). The locus coeruleus: Neurobiology of a central
noradrenergic nucleus. Progress in Neurobiology, 9, 147-196. doi:
10.1016/0301-0082(77)90016-8
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010).
Functional-anatomic fractionation of the brain's default network. Neuron, 65, 550-562.
doi:10.1016/j.neuron.2010.02.005
Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and
self-generated thought: Component processes, dynamic control, and clinical relevance.
Annals of the New York Academy of Sciences, 1316, 29-52. doi:10.1111/nyas.12360
Ansburg, P. I., & Hill, K. (2003). Creative and analytic thinkers differ in their use of attentional
resources. Personality and Individual Differences, 34, 1141-1152.
doi:10.1016/S0191-8869(02)00104-6
Aston-Jones, G., & Bloom, F. E. (1981). Nonrepinephrine-containing locus coeruleus neurons in
behaving rats exhibit pronounced responses to non-noxious environmental stimuli. Journal
of Neuroscience, 1, 887-900.
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 25
Aston-Jones, G., & Cohen, J. D. (2005a). Adaptive gain and the role of the locus
coeruleus-norepinephrine system in optimal performance. Journal of Comparative
Neurology, 493, 99-110. doi:10.1002/cne.20723
Aston-Jones, G., & Cohen, J. D. (2005b). An integrative theory of locus
coeruleus-norepinephrine function: Adaptive gain and optimal performance. Annual
Review of Neuroscience, 28, 403-450. doi:10.1146/ annurev.neuro.28.061604.135709
Aston-Jones, G., & Waterhouse, B. (2016). Locus coeruleus: From global projection system to
adaptive regulation of behavior. Brain Research, 1645, 75-78.
doi:10.1016/j.brainres.2016.03.001
Baas, M., Nijstad, B. A., Boot, N. C., & De Dreu, C. K. (2016). Mad genius revisited:
Vulnerability to psychopathology, biobehavioral approach-avoidance, and creativity.
Psychological Bulletin, 142, 668-692. doi:10.1037/bul0000049
Bar, M., & Neta, M. (2006). Humans prefer curved visual objects. Psychological Science, 17(8),
645-648. doi:10.1111/j.1467-9280.2006.01759.x
Bar, M., & Neta, M. (2007). Visual elements of subjective preference modulate amygdala
activation. Neuropsychologia, 45(10), 2191-2200.
doi:10.1016/j.neuropsychologia.2007.03.008
Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based
meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value.
NeuroImage, 76, 412-427. doi:10.1016/j.neuroimage.2013.02.063
Basadur, M., Graen, G. B., & Green, S. G. (1982). Training in creative problem solving: Effects
on ideation and problem finding and solving in an industrial research organization.
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 26
Organizational Behavior and Human Performance, 30, 41-70.
doi:10.1016/0030-5073(82)90233-1
Beaty, R. E., Benedek, M., Kaufman, S. B., & Silvia, P. J. (2015). Default and executive network
coupling supports creative idea production. Scientific Reports, 5, 10964.
doi:10.1038/srep10964
Beaty, R. E., Benedek, M., Silvia, P. J., & Schacter, D. L. (2016). Creative cognition and brain
network dynamics. Trends in Cognitive Sciences, 20, 87-95. doi:10.1016/j.tics.2015.10.004
Benedek, M., Jauk, E., Sommer, M., Arendasy, M., & Neubauer, A. C. (2014). Intelligence,
creativity, and cognitive control: The common and differential involvement of executive
functions in intelligence and creativity. Intelligence, 46, 73-83.
doi:10.1016/j.intell.2014.05.007
Berridge, C. W., & Waterhouse, B. D. (2003). The locus coeruleus–noradrenergic system:
Modulation of behavioral state and state-dependent cognitive processes. Brain Research
Reviews, 42, 33-84. doi:10.1016/S0165-0173(03)00143-7
Beversdorf, D. Q. (2013). Pharmacological effects on creativity. In O. Vartanian, A. S. Bristol, &
J. C. Kaufman (2013). Neuroscience of creativity (pp. 159-173). Cambridge, MA: The
MIT Press.
Boccia, M., Piccardi, L., Palermo, L., Nori, R., & Palmiero, M. (2015). Where do bright ideas
occur in our brain? Meta-analytic evidence from neuroimaging studies of domain-specific
creativity. Frontiers in Psychology, 6, 1195. doi:10.3389/fpsyg.2015.01195
Boot, N., Baas, M., van Gaal, S., Cools, R., & De Dreu, C. K. W. (2017). Creative cognition and
dopaminergic modulation of fronto-striatal networks: Integrative review and research
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 27
agenda. Neuroscience and Biobehavioral Reviews, 78, 13-23.
doi:10.1016/j.neubiorev.2017.04.007
Bouret, S., & Sara, S. J. (2005). Network reset: A simplified overarching theory of locus
coeruleus noradrenaline function. Trends in Neurosciences, 28, 574-582.
doi:10.1016/j.tins.2005.09.002
Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate
problems. Behavior Research Methods, 35, 634-639. doi:10.3758/BF03195543
Breedlove, S. M., Watson, N. V., & Rosenzweig, M. R. (2010). Biological psychology (6th Ed.).
Sunderland, MA: Sinauer Associates.
Brown, S., Gao, X., Tisdelle, L., Eickhoff, S. B., & Liotti, M. (2011). Naturalizing aesthetics:
Brain areas for aesthetic appraisal across sensory modalities. NeuroImage, 58(1), 250-258.
doi:10.1016/j.neuroimage.2011.06.012
Brun, P., Suaud-Chagny, M. F., Gonon, F., & Buda, M. (1993). In vivo noradrenaline release
evoked in the anteroventral thalamic nucleus by locus coeruleus activation: An
electrochemical study. Neuroscience, 52, 961-972. doi:10.1016/0306-4522(93)90543-O
Buckley, M. J., Mansouri, F. A., Hoda, H., Mahboubi, M., Browning, P. G. F., Kwok, S.
C., . . . Tanaka, K. (2009). Dissociable components of rule-guided behavior depend on
distinct medial and prefrontal regions. Science, 325, 52-58. doi:10.1126/science.1172377
Camerer, C. F. (2013). Goals, methods, and progress in neuroeconomics. Annual Review of
Economics, 5, 425-455. doi:10.1146/annurev-economics-082012-123040
Camille, N., Griffiths, C. A., Vo, K., Fellows, L. K., & Kable, J. W. (2011). Ventromedial frontal
lobe damage disrupts value maximization in humans. Journal of Neuroscience, 31,
7527-7532. doi:10.1523/JNEUROSCI.6527-10.2011
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 28
Campbell, D. T. (1960). Blind variation and selective retentions in creative thought as in other
knowledge processes. Psychological Review, 67, 380-400. doi:10.1037/h0040373
Campbell, H. L., Tivarus, M. E., Hillier, A., & Beversdorf, D. Q. (2008). Increased task
difficulty results in greater impact of noradrenergic modulation of cognitive flexibility.
Pharmacology Biochemistry and Behavior, 88, 222-229. doi:10.1016/j.pbb.2007.08.003
Chatterjee, A., & Vartanian, O. (2014). Neuroaesthetics. Trends in Cognitive Sciences, 18,
370-375. doi:10.1016/j.tics.2014.03.003
Chatterjee, A., & Vartanian, O. (2016). Neuroscience of aesthetics. Annals of the New York
Academy of Sciences, 1369, 172-194. doi:10.1111/nyas.13035
Christian, B., & Griffiths, T. (2016). Algorithms to live by: The computer science of human
decisions. New York: Macmillan.
Christoff, K., Irving, Z. C., Fox, K. C., Spreng, R. N., & Andrews-Hanna, J. R. (2016).
Mind-wandering as spontaneous thought: A dynamic framework. Nature Reviews
Neuroscience, 17, 718-731. doi:10.1038/nrn.2016.113
Churchland, A. K., Kiani, R., & Shadlen, M. N. (2008). Decision-making with multiple
alternatives. Nature Neuroscience, 11, 693-702. doi:10.1038/nn.2123
Cinzia, D. D., & Vittorio, G. (2009). Neuroaesthetics: A review. Current Opinion in
Neurobiology, 19, 682-687. doi:10.1016/j.conb.2009.09.001
Clithero, J. A., & Rangel, A. (2014). Informatic parcellation of the network involved in the
computation of subjective value. Social Cognitive and Affective Neuroscience, 9,
1289-1302. doi:10.1093/scan/nst106
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 29
Cocchi, L., Zalesky, A., Fornito, A., & Mattingley, J. B. (2013). Dynamic cooperation and
competition between brain systems during cognitive control. Trends in Cognitive Sciences,
17, 493-501. doi:10.1016/j.tics.2013.08.006
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human
brain manages the trade-off between exploitation and exploration. Philosophical
Transactions of the Royal Society of London B: Biological Sciences, 362, 933-942.
doi:10.1098/rstb.2007.2098
Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain:
From environment to theory of mind. Neuron, 58, 306-324.
doi:10.1016/j.neuron.2008.04.017
Devilbiss, D. M., & Waterhouse, B. D. (2000). Norepinephrine exhibits two distinct profiles of
action on sensory cortical neuron responses to excitatory synaptic stimuli. Synapse, 37,
273-282.
Diedrich, J., Benedek, M., Jauk, E., & Neubauer, A. C. (2015). Are creative ideas novel and
useful? Psychology of Aesthetics, Creativity, and the Arts, 9, 35-40. doi:10.1037/a0038688
Dietrich, A., & Haider, H. (2016). A neurocognitive framework for human creative thought.
Frontiers in Psychology, 7, 2078. doi:10.3389/fpsyg.2016.02078
Dorfman, L., Martindale, O., Gassimova, V., & Vartanian, O. (2008). Creativity and speed of
information processing: A double dissociation involving elementary versus inhibitory
cognitive tasks. Personality and Individual Differences, 44, 1382-1390.
doi:10.1016/j.paid.2007.12.006
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 30
Eldar, E., Niv, Y., & Cohen, J. D. (2016). Do you see the forest or the tree? Neural gain and
breadth versus focus in perceptual processing. Psychological Science, 27, 1632-1643.
doi:10.1177/0956797616665578
Eldar, E., Cohen, J. D., & Niv, Y. (2013). The effects of neural gain on attention and learning.
Nature Neuroscience, 16, 1146-1153. doi:10.1038/nn.3428
Ellamil, M., Dobson, C., Beeman, M., & Christoff, K. (2012). Evaluative and generative modes
of thought during the creative process. NeuroImage, 59, 1783-1794.
doi:10.1016/j.neuroimage.2011.08.008
Foote, S. L., & Morrison, J. H. (1987). Extrathalamic modulation of cortical function. Annual
Review of Neuroscience, 10, 67-95. doi:10.1146/annurev.ne.10.030187.000435
Foote, S.L., Berridge, C. W., Adams, L. M., & Pineda, J. A. (1991). Electrophysiological
evidence for the involvement of the locus coeruleus in alerting, orienting, and attending
neurobiology of the locus coeruleus. Progress in Brain Research, 88, 521-532.
doi:10.1016/S0079-6123(08)63831-5
Gabora, L. (2011). An analysis of the Blind Variation and Selective Retention (BVSR) theory of
creativity. Creativity Research Journal, 23, 155-165. doi: 10.1080/10400419.2011.571187
Gold, J. I., & Shadlen, M. N. (2007). The neural basis of decision making. Annual Review of
Neuroscience, 30, 535-574. doi:10.1146/annurev.neuro.29.051605.113038
Gonen-Yaacovi, G., de Souza, L. C., Levy, R., Urbanski, M., Josse, G., & Volle, E. (2013).
Rostral and caudal prefrontal contribution to creativity: A meta-analysis of functional
imaging data. Frontiers in Human Neuroscience, 7, 465. doi:10.3389/fnhum.2013.00465
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 31
Grueschow, M., Polania, R., Hare, T. A., & Ruff, C. C. (2015). Automatic versus
choice-dependent value representations in the human brain. Neuron, 85, 874-885.
doi:10.1016/j.neuron.2014.12.054
Hao, N., Ku, Y., Liu, M., Hu, Y., Grabner, R. H., & Fink, A. (2016). Reflection enhances
creativity: Beneficial effects of idea evaluation on idea generation. Brain and Cognition,
103, 30-37. doi: 10.1016/j.bandc.2016.01.005
Hare, T. A., Camerer, C. F., & Rangel, A. (2009). Self-control in decision-making involves
modulation of the vmPFC valuation system. Science, 324, 646-648.
doi:10.1126/science.1169957
Hecht, P. M., Will, M. J., Schachtman, T. R., Welby, L. M., & Beversdorf, D. Q. (2014).
Beta-adrenergic antagonist effects on a novel cognitive flexibility task in rodents.
Behavioural Brain Research, 260, 148-154. doi:10.1016/j.bbr.2013.11.041
Heilman, K. M. (2016). Possible brain mechanisms of creativity. Archives of Clinical
Neuropsychology, 31, 285-296. doi:10.1093/arclin/acw009
Heilman, K. M., Nadeau, S. E., & Beversdorf, D. O. (2003). Creative innovation: Possible brain
mechanisms. Neurocase, 9, 369-379. doi:10.1076/neur.9.5.369.16553
Hervé-Minvielle, A., & Sara, S. J. (1995). Rapid habituation of auditory responses of locus
coeruleus cells in anaesthetized and awake rats. NeuroReport, 6, 1363-1368. doi:
10.1097/00001756-199507100-00001
Hogeveen, J., Hauner, K. K., Chau, A., Krueger, F., & Grafman, J. (2016). Impaired valuation
leads to increased apathy following ventromedial prefrontal cortex damage. Cerebral
Cortex, bhv317, 1-8. doi:10.1093/cercor/bhv317
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 32
Hutcherson, C. A., Bushong, B., & Rangel, A. (2015). A neurocomputational model of altruistic
choice and its implications. Neuron, 87, 451-462. doi:10.1016/j.neuron.2015.06.031
Ikeda, T., Matsuyoshi, D., Sawamoto, N., Fukuyama, H., & Osaka, N. (2015). Color harmony
represented by activity in the medial orbitofrontal cortex and amygdala. Frontiers in
Human Neuroscience, 9, 382. doi:10.3389/fnhum.2015.00382
Ishizu, T., & Zeki, S. (2011). Toward a brain-based theory of beauty. PLoS One, 6, e21852.
doi:10.1371/journal.pone.0021852
Jacobsen, T., Schubotz, R. I., Höfel, L., & Cramon, D. Y. (2006). Brain correlates of aesthetic
judgment of beauty. NeuroImage, 29, 276-285. doi:10.1016/j.neuroimage.2005.07.010
Jauk, E., Benedek, M., Dunst, B., & Neubauer, A. C. (2013). The relationship between
intelligence and creativity: New support for the threshold hypothesis by means of empirical
breakpoint detection. Intelligence, 41, 212-221. doi:10.1016/j.intell.2013.03.003
Jauk, E., Benedek, M., & Neubauer, A. C. (2014). The road to creative achievement: A latent
variable model of ability and personality predictors. European Journal of Personality, 28,
95-105. doi:10.1002/per.1941
Jepma, M., & Nieuwenhuis, S. (2011). Pupil diameter predicts changes in the
exploration-exploitation trade-off: Evidence for the adaptive gain theory. Journal of
Cognitive Neuroscience, 23, 1587-1596. doi:10.1162/jocn.2010.21548
Jung, R. E., & Vartanian, O. (Eds.).(in press). Cambridge handbook of the neuroscience of
creativity. New York: Cambridge University Press.
Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during
intertemporal choice. Nature Neuroscience, 10, 1625-1633. doi:10.1038/nn2007
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 33
Kable, J. W., & Glimcher, P. W. (2009). The neurobiology of decision: Consensus and
controversy. Neuron, 63, 733-745. doi:10.1016/j.neuron.2009.09.003
Kim, H., Adolphs, R., O’Doherty, J. P., & Shimojo, S. (2007). Temporal isolation of neural
processes underlying face preference decisions. Proceedings of the National Academy of
Sciences, 104, 18253-18258. doi:10.1073/pnas.0703101104
Kirk, U., Skov, M., Hulme, O., Christensen, M. S., & Zeki, S. (2009). Modulation of aesthetic
value by semantic context: An fMRI study. NeuroImage, 44, 1125-1132.
doi:10.1016/j.neuroimage.2008.10.009
Konovalov, A., & Krajbich, I. (2016). Over a decade of neuroeconomics: What have we learned.
Organizational Research Methods. 10-26. doi:10.1177/1094428116644502
Kounios, J., Frymiare, J. L., Bowden, E. M., Fleck, J. I., Subramaniam, K., Parrish, T. B., &
Jung-Beeman, M. (2006). The prepared mind neural activity prior to problem presentation
predicts subsequent solution by sudden insight. Psychological Science, 17, 882-890.
doi:10.1111/j.1467-9280.2006.01798.x
Kyaga, S., Lichtenstein, P., Boman, M., Hultman, C., Långström, N., & Landén, M. (2011).
Creativity and mental disorder: Family study of 300,000 people with severe mental
disorder. The British Journal of Psychiatry, 199, 373-379. doi:10.1192/bjp.bp.110.085316
Levy, D. J., & Glimcher, P. W. (2012). The root of all value: a neural common currency for
choice. Current Opinion in Neurobiology, 22, 1027–1038.
http://doi.org/10.1016/j.conb.2012.06.001
Liu, S., Erkkinen, M. G., Healey, M. L., Xu, Y., Swett, K. E., Chow, H. M., & Braun, A. R.
(2015). Brain activity and connectivity during poetry composition: Toward a
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 34
multidimensional model of the creative process: Composing poetry: Process, product,
expertise. Human Brain Mapping, 36, 3351-3372. doi:10.1002/hbm.22849
Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453,
869-878. doi:10.1038/nature06976
Lubart, T. I., & Sternberg, R. J. (1995). An investment approach to creativity: Theory and data.
In S. M. Smith, T. B.Ward, & R. A. Finke (Eds.), The creative cognition approach (pp.
269-302). Cambridge, MA: MIT Press.
Martindale, C. (1984). The pleasures of thought: A theory of cognitive hedonics. The Journal of
Mind and Behavior, 5, 49-80.
Martindale, C., Anderson, K., Moore, K., & West, A. N. (1996). Creativity, oversensitivity, and
rate of habituation. Personality and Individual Differences, 20, 423-427.
doi:10.1016/0191-8869(95)00193-X
Martindale, C., & Armstrong, J. (1974). The relationship of creativity to cortical activation and
its operant control. The Journal of Genetic Psychology, 124, 311-320.
doi:10.1080/00221325.1974.10532293
Martindale, C., & Greenough, J. (1973). The differential effect of increased arousal on creative
and intellectual performance. The Journal of Genetic Psychology, 123, 329-335.
doi:10.1080/00221325.1973.10532692
Martindale, C., & Hasenfus, N. (1978). EEG differences as a function of creativity, stage of the
creative process, and effort to be original. Biological Psychology, 6, 157-167.
doi:10.1016/0301-0511(78)90018-2
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 35
Mather, M., Clewett, D., Sakaki, M., & Harley, C. W. (2015). Norepinephrine ignites local hot
spots of neuronal excitation: How arousal amplifies selectivity in perception and memory.
Behavioral and Brain Sciences, 39, 1-100. doi:10.1017/S0140525X15000667
Mather, M., & Harley, C. W. (2016). The locus coeruleus: Essential for maintaining cognitive
function and the aging brain. Trends in Cognitive Sciences, 20(3), 214-226.
doi:10.1016/j.tics.2016.01.001
Mather, M., Joo Yoo, H., Clewett, D. V., Lee, T. H., Greening, S. G., Ponzio, A., . . . Thayer, J.
F. (2017). Higher locus coeruleus MRI contrast is associated with lower parasympathetic
influence over heart rate variability. NeuroImage, 150, 329-335.
doi:10.1016/j.neuroimage.2017.02.025
Mayseless, N., Aharon-Peretz, J., & Shamay-Tsoory, S. (2014). Unleashing creativity: The role
of left temporoparietal regions in evaluating and inhibiting the generation of creative ideas.
Neuropsychologia, 64, 157-168. doi: 10.1016/j.neuropsychologia.2014.09.022
Mittner, M., Hawkins, G. E., Boekel, W., & Forstmann, B. U. (2016). A neural model of mind
wandering. Trends in Cognitive Sciences, 20(8), 570-578. doi:10.1016/j.tics.2016.06.004
McClure, S. M., Laibson, D. I., Loewenstein, G., & Cohen, J. D. (2004). Separate neural systems
value immediate and delayed monetary rewards. Science, 306, 503-507.
doi:10.1126/science.1094492.
McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., & Montague, P. R. (2004).
Neural correlates of behavioral preference for culturally familiar drinks. Neuron, 44,
379-387. doi:10.1016/j.neuron.2004.09.019
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 36
Mendelsohn, G. A., & Griswold, B. B. (1964). Differential use of incidental stimuli in problem
solving as a function of creativity. The Journal of Abnormal and Social Psychology, 68,
431-436. doi:10.1037/h0040166
Moore, R. Y., & Bloom, F. E. (1979). Central catecholamine neuron systems: Anatomy and
physiology of the norepinephrine and epinephrine systems. Annual Review of Neuroscience,
2, 113-168. doi:10.1146/annurev.ne.02.030179.000553
Murphy, P. R., O'Connell, R. G., O'Sullivan, M., Robertson, I. H., & Balsters, J. H. (2014). Pupil
diameter covaries with BOLD activity in human locus coeruleus. Human Brain Mapping,
35, 4140-4154. doi:10.1002/hbm.22466
Murphy, P. R., Robertson, I. H., Balsters, J. H., & O'Connell, R. G. (2011). Pupillometry and P3
index the locus coeruleus-noradrenergic arousal function in humans. Psychophysiology, 48,
1532-1543. doi:10.1111/j.1469-8986.2011.01226.x
Nieuwenhuis, S., Aston-Jones, G., & Cohen, J. D. (2005). Decision making, the P3, and the
locus coeruleus-norepinephrine system. Psychological Bulletin, 131(4), 510-532.
doi:10.1037/0033-2909.131.4.510
Nusbaum, E. C., & Silvia, P. J. (2011). Are intelligence and creativity really so different? Fluid
intelligence, executive processes, and strategy use in divergent thinking. Intelligence, 39,
36–45. doi:10.1016/j.intell.2010.11.002
O'Doherty, J., Winston, J., Critchley, H., Perrett, D., Burt, D. M., & Dolan, R. J. (2003). Beauty
in a smile: The role of medial orbitofrontal cortex in facial attractiveness.
Neuropsychologia, 41, 147-155. doi:10.1016/S0028-3932(02)00145-8
Padoa-Schioppa, C. (2011). Neurobiology of economic choice: A good-based model. Annual
Review of Neuroscience, 34, 333-359. doi:10.1146/annurev-neuro-061010-113648
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 37
Padoa-Schioppa, C., & Assad, J. A. (2006). Neurons in the orbitofrontal cortex encode economic
value. Nature, 441, 223-226. doi:10.1038/nature04676
Padoa-Schioppa, C., & Cai, X. (2011). The orbitofrontal cortex and the computation of
subjective value: Consolidated concepts and new perspectives. Annals of the New York
Academy of Sciences, 1239, 130-137. doi:10.1111/nyas.2011.1239.issue-1
Pearce, M. T., Zaidel, D. W., Vartanian, O., Skov, M., Leder, H., Chatterjee, A., & Nadal, M.
(2016). Neuroaesthetics: The cognitive neuroscience of aesthetic experience. Perspectives
on Psychological Science, 11, 265-279. doi:10.1177/1745691615621274
Pearson, J. M., Hayden, B. Y., Raghavachari, S., & Platt, M. L. (2009). Neurons in posterior
cingulate cortex signal exploratory decisions in a dynamic multioption choice task. Current
Biology, 19, 1532-1537. doi:10.1016/j.cub.2009.07.048
Pearson, J. M., Heilbronner, S. R., Barack, D. L., Hayden, B. Y., & Platt, M. L. (2011). Posterior
cingulate cortex: Adapting behavior to a changing world. Trends in Cognitive Sciences, 15,
143-151. doi:10.1016/j.tics.2011.02.002
Porrino, L. J., & Goldman-Rakic, P. S. (1982). Brainstem innervation of prefrontal and anterior
cingulate cortex in the rhesus monkey revealed by retrograde transport of HRP. Journal of
Comparative Neurology, 205, 63-76. doi:10.1002/cne.902050107
Rajkowski, J., Kubiak, P., & Aston-Jones, G. (1994). Locus coeruleus activity in monkey: Phasic
and tonic changes are associated with altered vigilance. Brain Research Bulletin, 35,
607-616. doi:10.1016/0361-9230(94)90175-9
Rangel, A., Camerer, C., & Montague, P. R. (2008). A framework for studying the neurobiology
of value-based decision making. Nature Reviews Neuroscience, 9, 545-556.
doi:10.1038/nrn2357
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 38
Rangel, A., & Hare, T. (2010). Neural computations associated with goal-directed choice.
Current Opinion in Neurobiology, 20, 262-270. doi:10.1016/j.conb.2010.03.001
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016). Diffusion decision model:
Current issues and history. Trends in Cognitive Sciences, 20, 260-281.
doi:10.1016/j.tics.2016.01.007
Rich, E. L., & Wallis, J. D. (2016). Decoding subjective decisions from orbitofrontal cortex.
Nature Neuroscience, 19, 973-980. doi:10.1038/nn.4320
Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research
Journal, 24, 92-96. doi:10.1080/10400419.2012.650092
Russell, J. (1976). Utilization of irrelevant information by high and low creatives. Psychological
Reports, 39, 105-106. doi:10.2466/pr0.1976.39.1.105
Salimpoor, V. N., Benovoy, M., Larcher, K., Dagher, A., & Zatorre, R. J. (2011). Anatomically
distinct dopamine release during anticipation and experience of peak emotion to music.
Nature Neuroscience, 14, 257-262. doi:10.1038/nn.2726
Salimpoor, V. N., van den Bosch, I., Kovacevic, N., McIntosh, A. R., Dagher, A., & Zatorre, R. J.
(2013). Interactions between the nucleus accumbens and auditory cortices predict music
reward value. Science, 340, 216-219. doi:10.1126/science.1231059
Salimpoor, V. N., & Zatorre, R. J. (2013). Neural interactions that give rise to musical pleasure.
Psychology of Aesthetics, Creativity, and the Arts, 7, 62-75. doi:10.1037/a0031819.supp
Sara, S. J. (2009). The locus coeruleus and noradrenergic modulation of cognition. Nature
Reviews Neuroscience, 10, 211-223. doi:10.1038/nrn2573
Sara, S. J., & Bouret, S. (2012). Orienting and reorienting: The locus coeruleus mediates
cognition through arousal. Neuron, 76, 130-141. doi:10.1016/j.neuron.2012.09.011
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 39
Shadlen, M. N., & Kiani, R. (2013). Decision making as a window on cognition. Neuron, 80,
791-806. doi:10.1016/j.neuron.2013.10.047
Shadlen, M. N., & Shohamy, D. (2016). Decision making and sequential sampling from memory.
Neuron, 90, 927-939. doi:10.1016/j.neuron.2016.04.036
Shusterman, R. (1997). The end of aesthetic experience. The Journal of Aesthetics and Art
Criticism, 55, 29-41. doi:10.2307/431602
Simonton, D. K. (1999). Creativity as blind variation and selective retention: Is the creative
process Darwinian? Psychological Inquiry, 10, 309-328. doi:10.1207
Simonton, D. K. (2014). The mad-genius paradox: Can creative people be more mentally healthy
but highly creative people more mentally ill. Perspectives on Psychological Science, 9,
470-480. doi:10.1177/1745691614543973
Smallwood, J., & Schooler, J. W. (2015). The science of mind wandering: Empirically
navigating the stream of consciousness. Annual Review of Psychology, 66, 487-518.
doi:10.1146/annurev-psych-010814-015331
Smith, A., Bernheim, B. D., Camerer, C., & Rangel, A. (2014). Neural activity reveals
preferences without choices. American Economic Journal: Microeconomics, 6, 1-36.
doi:10.1257/mic.6.2.1
Smith, P. L., & Ratcliff, R. (2004). Psychology and neurobiology of simple decisions. Trends in
Neurosciences, 27, 161-168. doi:10.1016/j.tins.2004.01.006
Sternberg, R. J. (1999). Handbook of creativity. Cambridge, MA: Cambridge University Press.
Sternberg, R. J. (2006). The nature of creativity. Creativity Research Journal, 18, 87-98. doi:
10.1207/s15326934crj1801_10
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 40
Sternberg, R. J. (2012). The assessment of creativity: An investment-based approach. Creativity
Research Journal, 24, 3-12. doi:10.1080/10400419.2012.652925
Tsukahara, J. S., Harrison, T. L., & Engle, R. W. (2016). The relationship between baseline pupil
size and intelligence. Cognitive Psychology, 91, 109-123.
doi:10.1016/j.cogpsych.2016.10.001
Tusche, A., Bode, S., & Haynes, J. D. (2010). Neural responses to unattended products predict
later consumer choices. Journal of Neuroscience, 30, 8024-8031.
doi:10.1523/JNEUROSCI.0064-10.2010
Uddin, L. Q. (2015). Salience processing and insular cortical function and dysfunction. Nature
Reviews Neuroscience, 16, 55-61. doi:10.1038/nrn3857
Usher, M., Cohen, J. D., Servan-Schreiber, D., Rajkowski, J., & Aston-Jones, G. (1999). The
role of locus coeruleus in the regulation of cognitive performance. Science, 283, 549-554.
doi:10.1126/science.283.5401.549
Vartanian, O. (2009). Variable attention facilitates creative problem solving. Psychology of
Aesthetics, Creativity, and the Arts, 3, 57-59. doi:10.1037/a0014781
Vartanian, O. (2011). Decision junctures in the creative process. In O. Vartanian, & D. R.
Mandel (Eds.), Neuroscience of decision making (pp. 311–327). New York, NY:
Psychology Press.
Vartanian, O., Bristol, A., & Kaufman, J. C. (Eds.).(2013). Neuroscience of creativity. Cambridge,
MA: MIT Press.
Vartanian, O., Martindale, C., & Kwiatkowski, J. (2007). Creative potential, attention, and speed
of information processing. Personality and Individual Differences, 43, 1470-1480.
doi:10.1016/j.paid.2007.04.027
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
NEUROECONOMICS AND CREATIVITY 41
Vartanian, O., & Skov, M. (2014). Neural correlates of viewing paintings: Evidence from a
quantitative meta-analysis of functional magnetic resonance imaging data. Brain and
Cognition, 87, 52-56. doi:10.1016/j.bandc.2014.03.004
Zabelina, D. L., & Andrews-Hanna, J. R. (2016). Dynamic network interactions supporting
internally-oriented cognition. Current Opinion in Neurobiology, 40, 86-93.
doi:10.1016/j.conb.2016.06.014
Zabelina, D. L., O'Leary, D., Pornpattananangkul, N., Nusslock, R., & Beeman, M. (2015).
Creativity and sensory gating indexed by the P50: Selective versus leaky sensory gating in
divergent thinkers and creative achievers. Neuropsychologia, 69, 77-84.
doi:10.1016/j.neuropsychologia.2015.01.034
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
The copyright holder for this preprint (which wasthis version posted September 5, 2017. ; https://doi.org/10.1101/184754doi: bioRxiv preprint
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Figure 1. Representation of Value in the Human Brain.
Notes. Regions of the brain that represent value, identified via a meta-analysis of neuroimaging
studies. ALE = Activation Likelihood Estimation (brighter regions indicate greater signal
strength across studies included in the meta-analysis); dPCC = dorsal posterior cingulate cortex;
vPCC = ventral posterior cingulate cortex; VSTR = ventral striatum; VMPFC = ventromedial
prefrontal cortex. Reproduced with permission from Clithero and Rangel (2014).
.CC-BY-NC-ND 4.0 International licenseunder anot certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available
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Figure 2. The Locus Coeruleus-Norepinephrine (LC-NE) System.
Notes. Reproduced with permission from Breedlove, Watson, and Rosenzweig (2010).
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